It is not uncommon to see the amount of data associated with a business venture grow at an exponential pace. As the size of the database grows, so do the resources and time involved in processing the data. Thus, the data is sometimes stored and managed within the context of an Enterprise Data Warehouse (EDW).
Complex analytics may be applied to the stored data, with a periodicity that varies according to the nature of the data, the nature of the application, and the time of day. For example, in the retail world, individual store managers may operate report generation tools, which issue hundreds of complex queries, at the start and end of their workdays. Within a single time zone, this activity may amount to a significant analytical load being placed on the EDW at certain times of the day, such as after stores open, and after they close. Other periods of significant analytical load (caused by financial report generation) might occur at the end of the week, the end of the month, or the end of the quarter. End of year processing and tax period processing might also constitute points of significant analytical load. Given the frequency of these activities, there may be points in time where large data loads and extensive analytical processing conspire to overwhelm the system.